Kabushiki Kaisha Toshiba

Canon Medical Systems Corporation

Toshiba Infrastructure Systems & Solutions Corporation

Kenzo Isogawa

Takashi Ida

Nobuyuki Matsumoto

Toshimitsu Kaneko

Hidenori Takeshima

Yasunori Taguchi

Takeshi Mita

Koji Yamamoto

Toshiyuki Ono

Yoshihiro Ohmori

Satoshi Kawata

Koichi Masukura

Nao Mishima

Tomoyuki Takeguchi

Shuhei Nitta

Atsushi Matsumura

Kenichi Shimoyama

Mai Murashima

Masahiro Baba

Takeshi Morino

Goh Itoh

Ryusuke Hirai

Masataka Shiratsuchi

Yoshiharu Momonoi

Kazuyasu Ohwaki

According to one embodiment, a medical image diagnostic apparatus includes processing circuitry. The processing circuitry inputs a noise correlation map and a medical image or an intermediate image to a learned model that is functioned to generate a denoise image, in which noise of the medical image or noise of the intermediate image is reduced, based on the medical image generated based on data collected with respect to a subject or the intermediate image at a front stage for generating the medical image and the noise correlation map correlated with the noise included in the medical image or the intermediate image, and generates the denoise image, in which the noise of the medical image or the noise of the intermediate image is reduced.

Medical image diagnostic apparatus

According to one embodiment, a wall detection device includes an acquirer and processing circuitry. The acquirer acquires a point cloud, which includes a series of coordinates of a plurality of points corresponding to a first wall and a second wall that oppose each other. The processing circuitry detects a first detected wall and a second detected wall based on a model and the acquired point cloud, the model representing a first plane which corresponds to the first detected wall and indicates a surface of the first wall and a second plane which corresponds to the second detected wall and indicates a surface of the second wall, and the model representing the first plane and the second plane being parallel to each other.

Wall detection device, transport vehicle, and wall detection method

According to one embodiment, a signal processing apparatus includes a memory and a processing circuit. The memory stores a learned model for generating restoration data by restoring deterioration of a signal based on data of the signal and data related to a position of a sample of the signal. The processing circuit inputs data of the signal and data related to the position to the learned model. The processing circuit generates the restoration data by using the learned model.

Signal processing apparatus, distance measuring apparatus, and distance measuring method

In one embodiment, an image processing system includes a memory and processing circuitry. The memory is configured to store a predetermined program. The processing circuitry is configured, by executing the predetermined program, to perform processing on an input image by exploiting a neural network having an input layer, an output layer, and an intermediate layer provided between the input layer and the output layer, the input image being inputted to the input layer, and adjust an internal parameter based on data related to the input image, while performing the processing on the input image after training of the neural network, the internal parameter being at least one internal parameter of at least one node included in the intermediate layer, and the input parameter having been calculated by the training of the neural network.

Neural network medical image system

A magnetic resonance imaging apparatus according to an embodiment includes an MRI system and a processing circuitry. The MRI system includes a receiving coil to receive a magnetic resonance signal. The processing circuitry is configured to generate an image based on the magnetic resonance signal, the image including a plurality of pixels; calculate a feature value corresponding to a signal value of the pixel; correct the feature values based on a sensitivity of the receiving coil; and reduce noise in the image based on distribution of the corrected feature values.

Magnetic resonance imaging apparatus, image processing apparatus, and image processing method

Image processing system and medical information processing system

According to one embodiment, a distance measuring apparatus includes a processing circuit and a memory. The processing circuit generates a distance image by measuring distances to an obstacle by using the time until the emitted light is reflected from the obstacle and returned as reflected light. The processing circuit generates an intensity image by measuring the intensity of the reflected light or the intensity of the environmental light. The memory stores a learned model for generating a denoise image, in which noise of the distance image is reduced, based on the distance image and the intensity image. The processing circuit inputs the distance image and the intensity image to the learned model, and generates the

Distance measuring apparatus, vibration measuring apparatus, and industrial computed tomography apparatus

According to one embodiment, a magnetic resonance imaging apparatus includes processing circuitry. The processing circuitry is configured to apply a filter to each of a first real-part image and a first imaginary-part image of a first complex image generated based on acquired magnetic resonance data and thereby generate a second complex image that includes a second real-part image and a second imaginary-part image. The processing circuitry is configured to generate a phase image denoised by the filter, the denoised phase image generated based on the second real-part image and the second imaginary-part image. The processing circuitry is configured to generate an intensity image related to an absolute value of the first complex image based on pixel values of the denoised phase image, the first real-part image, and the first imaginary-part image.

Magnetic resonance imaging apparatus and medical complex image processing apparatus

An image processing apparatus includes an operating unit configured to calculate a tangent-line direction of an edge and a normal-line direction of the edge by using a vertical-direction derivative value and a horizontal-direction derivative value of a pixel value of a pixel in an input image; a converting unit configured to convert local coordinates positioned within a predetermined area with respect to a target pixel in the input image into rotated coordinates, by rotating the local coordinates according to an angle formed by the horizontal direction and the tangent-line direction of the edge; and a fitting unit configured to perform, at the rotated coordinates, a fitting process that employs a least-squares method by using a curved surface model expressed with the pixel value of the target pixel in the input image.

Techniques to suppress noises in an image to precisely extract shapes and edges

According to one embodiment, an image processing apparatus includes first and second computation portions, a selection portion, a projection portion, and a weighted averaging portion. The first computation portion is configured to obtain magnitudes of correlations between a first vector and plural basis vectors. The selection portion is configured to select basis vectors from the plural basis vectors. The projection portion is configured to select a second region, obtain a first projection vector by projecting the first vector onto a subspace formed by the selected basis vectors and obtain a second projection vector for each second region by projecting a second vector onto the subspace. The second computation portion is configured to compute a distance between the first and second projection vectors. The weighted averaging portion is configured to weighted average a pixel value of the second pixel to obtain an output pixel value of a first pixel.

Image processing apparatus and method

According to one embodiment, a liquid crystal display includes a display module and a backlight module configured to irradiate light on the display module. The backlight module includes a light guide plate, a first light source, a reflective plate, and a second light source. The light guide plate has a light output face at a position corresponding to a first small area on the display module. The first light source is configured to irradiate light toward the light guide plate from a side of the light guide plate in such a manner that the irradiated light reaches the first small area. The reflective plate is facing to the light guide plate, and the reflective plate has a reflective face configured to reflect light toward a second small area different from the first small area. The second light source is configured to irradiate light toward the reflective plate from a side of the reflective plate in such a manner that the irradiated light reaches the second small area.

Liquid crystal display and backlight module with tilting reflective surface

A degraded-image restoration method includes inputting a degraded image degraded by a predetermined degradation process; reading from a storage unit a filter coefficient in a filtering process of restoring the degraded image, in which an iteration formula for minimizing a square error between the degraded image and an image obtained by degrading a restored image by the degradation process is expanded corresponding to a predetermined number of iterations; and performing the filtering process using the filter coefficient with respect to the degraded image to obtain a restored image of the degraded image. Accordingly, a throughput in an apparatus that restores the degraded image can be reduced, thereby obtaining a preferable restored image.

Method of restoring degraded image, apparatus for restoring degraded image, and computer program product

A super-resolution device and method for setting at least one of a plurality of pixels included in image data as target pixels, the image data including pixels arranged in a screen and pixel values representing brightness, an area including the target pixel and peripheral pixels as a target area, and an area for searching pixel value change patterns in the target pixel area; calculating a difference between a first change pattern and second change pattern; comparing a difference between the first and second change patterns; calculating a pixel value of a super-resolution image having a number of pixels larger than a number of pixels included in the image data on the basis of a decimal-accuracy-vector, an extrapolated vector, and pixel values obtained from the image data.

Growing community of inventors

Kanagawa, Japan

Kenzo Isogawa